Efficient DPP sampling has been an active area of research over the
past decade. Several different approaches have been developed,
such as an algorithm based on the eigendecomposition of \(\) 
[1]}, [2]} as well as an approximate MCMC sampler
[3]} among others. For our problem, it is important
to be able to sample
from \(()\)  without having to actually construct the entire
matrix \(\) , and much faster than it takes to compute the full
inverse \(^{-1}\) . Moreover, being able to rapidly generate multiple independent
samples is crucial because of the iterative nature of the Randomized
Newton Method. A recently proposed DPP sampler satisfies all of these
conditions. We quote the time complexity of this method (the bounds
hold with high probability relative to the randomness of the algorithm).
[[4]}]
For a \(d\times d\)  p.s.d. matrix \(\)  let \(k=[|\hat{S}|]\)  where
\(\hat{S} \sim ()\) . Given \(\) , we can sample
